如何增强深度学习对图像明暗变化的鲁棒性，给我pytorch代码

以下是一个简单的 PyTorch 代码示例，用于增强深度学习对图像明暗变化的鲁棒性：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((.5,), (.5,))
])

# 加载数据集
trainset = datasets.MNIST('data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(.25)
        self.dropout2 = nn.Dropout2d(.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = nn.functional.log_softmax(x, dim=1)
        return output

# 定义训练函数
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == :
            print('Train Epoch: {} [{}/{} ({:.f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义设备和模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net().to(device)

# 定义优化器和训练参数
optimizer = optim.SGD(model.parameters(), lr=.01, momentum=.5)

# 训练模型
for epoch in range(1, 11):
    train(model, device, trainloader, optimizer, epoch)

这个代码示例使用了 PyTorch 框架，定义了一个简单的卷积神经网络模型，用于对 MNIST 数据集进行分类。在训练过程中，使用了随机梯度下降优化器，并在每个 epoch 结束时输出训练损失。这个代码示例并没有直接针对图像明暗变化的鲁棒性进行优化，但是可以通过调整模型结构和训练参数来实现这个目标。